Modeling Global and Local Interactions for Online Conversation Recommendation

The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories. While prior work mostly focuses on post-level recommendation, we aim to explore conversation context and model the interaction patterns therein. Furthermore, to characterize personal interests from interleaving user interactions, we learn (1) global interactions , represented by topic and discourse word clusters to reflect users’ content and pragmatic preferences, and (2) local interactions , encoding replying relations and chronological order of conversation turns to characterize users’ prior behavior. Built on collaborative filtering, our model captures global interactions via discovering word distributions to represent users’ topical interests and discourse behaviors, while local interactions are explored with graph-structured networks exploiting both reply structure and temporal features. Extensive experiments on three datasets from Twitter and Reddit show that our model coupling global and local interactions significantly outperforms the state-of-the-art model. Further analyses show that our model is able to capture meaningful features from global and local interactions, which results in its superior performance in conversation recommendation.

“Are You Ready?” Revisiting Needs Analysis for ESP Online Conversation Course

It is common to be found in an ESP class where the students are not fully engaged in the learning activities due to their lack of interests toward English. To attract the ESP students’ interests, authentic materials and meaningful activities need to be provided. Online learning can bring authentic materials to ESP students and give them meaningful activities as they can interact with native speakers of English through many online platforms. To fit the students’ online learning situations to their learning experiences, as well as the materials and activities to be given, a needs analysis should be carried out. The study focuses on discovering the ESP students’ preparedness in performing an online English conversation course by conducting a needs analysis. Questionnaires were given to 82 students and an interview was performed with 17 students to gain the data. The data collected in the form of percentages were analyzed by describing them qualitatively. From the analysis, it can be concluded that the ESP students were prepared to join the online English conversation course.

Sharing Private Space with Strangers: The Phenomenon of Couchsurfing App User

Couchsurfing is an application that connects persons who need lodging, with individuals who offer their house as a free place to stay. Persons who need lodging are called surfers and individuals who provide their house are called hosts. When hosts and surfers are just getting acquainted through the Couchsurfing app, they hold some uncertainty with each other. This study describes the experiences of both parties in their efforts to reduce uncertainty and build close relationships, even though they only met through digital applications and never face to face. This study uses a phenomenological approach with data search techniques in the form of in-depth interviews, direct observation, and data observation through the Couchsurfing application. The number of interviewees was 14 from various cities in Indonesia. To reduce uncertainty and build close relationships, hosts and surfers go through five stages; first, understanding and believing technology; second, pre-conversation; third, online conversation-1; fourth, face-to-face conversation; and fifth, online conversation-2. The description of these stages is summarized in the findings of this study. This finding also proves that the presence of technology may modify the previously known theories of interpersonal communication. Interpersonal communication not only takes place between two people directly but also involves technology as an intermediary.

Digital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics

Background Digital conversations can offer unique information into the attitudes of Hispanics with depression outside of formal clinical settings and help generate useful information for medical treatment planning. Our study aimed to explore the big data from open‐source digital conversations among Hispanics with regard to depression, specifically attitudes toward depression comparing Hispanics and non-Hispanics using machine learning technology. Methods Advanced machine‐learning empowered methodology was used to mine and structure open‐source digital conversations of self‐identifying Hispanics and non-Hispanics who endorsed suffering from depression and engaged in conversation about their tone, topics, and attitude towards depression. The search was limited to 12 months originating from US internet protocol (IP) addresses. In this cross-sectional study, only unique posts were included in the analysis and were primarily analyzed for their tone, topic, and attitude towards depression between the two groups using descriptive statistical tools. Results A total of 441,000 unique conversations about depression, including 43,000 (9.8%) for Hispanics, were posted. Source analysis revealed that 48% of conversations originated from topical sites compared to 16% on social media. Several critical differences were noted between Hispanics and non-Hispanics. In a higher percentage of Hispanics, their conversations portray “negative tone” due to depression (66% vs 39% non-Hispanics), show a resigned/hopeless attitude (44% vs. 30%) and were about ‘living with’ depression (44% vs. 25%). There were important differences in the author's determined sentiments behind the conversations among Hispanics and non-Hispanics. Conclusion In this first of its kind big data analysis of nearly a half‐million digital conversations about depression using machine learning, we found that Hispanics engage in an online conversation about negative, resigned, and hopeless attitude towards depression more often than non-Hispanic.

PELATIHAN KEMAMPUAN PUBLIC SPEAKING DI SMAN 100 JAKARTA

In this era of online conversation, it is undeniable that the ability to communicate between individuals must be prioritized. One of the important interpersonal communication to learn is the ability to speak in public. This skill should be taught from an early age in schools because public speaking is not only about delivering messages, but also how messages are structured, effective delivery styles and verbal and nonverbal language. This ability will support other communication skills in the future. The Community Service (PKM) activity at the Faculty of Communication at Tarumanagara University this time was held at SMA 100 Jakarta. The methods used are seminars and workshops. It is hoped that with this activity participants who are students can understand and practice public speaking well.Di era percakapan serba daring ini, tidak bisa dipungkiri bahwa kemampuan berkomunikasi antarpribadi tetap harus diutamakan. Salah satu komunikasi antarpribadi yang penting untuk dipelajari adalah kemampuan public speaking atau berbicara di depan umum. Keterampilan ini hendaknya diajarkan sejak dini di sekolah-sekolah karena public speaking tidak hanya bicara tentang penyampaian pesan, tapi juga bagaiman pesan disusun, gaya menyampaian yang efektif dan bahasa verbal dan nonverbal. Kemampuan ini akan mendukung kemahiran komunikasi lainnya di kemudian hari. Pada kegiatan Pengabdian kepada Masyarakat (PKM) Fakultas Ilmu Komunikasi Universitas Tarumanagara kali ini dilakukan di SMA 100 Jakarta. Metode yang dilakukan adalah seminar dan workshop. Diharapkan dengan kegiatan ini peserta yang adalah siswa-siswi dapat memahami dan mempraktikkan public speaking dengan baik.

Open up the Culture of Masculinity in Computer Technology for Gender and Diversity. A Conversation with Judy Wajcman. By Marion Mangelsdorf

The following interview is based on an online conversation that took place in February 2021 between Feminist Science and Technology Studies (STS) researchers Judy Wajcman and Marion Mangelsdorf. In 2004 Judy Wajcman published the STS classic TechnoFeminism, in which she analyses the fundamental presence of digital technologies and technological design processes. Wajcman discusses the range of feminist positions on the technological history of digitization and draws attention to the challenges that still exist today. She casts her decidedly critical eye on the gender issues as well as the racial bias that characterize digitization and assesses opportunities for cultural change.

Relationship Between Subjective Ratings of Answers and Behavioral and Autonomic Nervous Activities During Creative Problem-Solving via Online Conversation

Creative problem solving has been important for the advent of new technologies. In this study, we hypothesized that subjective ratings of answers should be useful for evaluating the answer quality in creative problem solving. To test this hypothesis and extract objective indicators of the subjective ratings of answers, we evaluated the relationship between subjective ratings of task performance and behavioral and autonomic nervous activities during a creative problem-solving task performed via online conversation. The task involved an answerer and a supporter, and in the experiment, each pair performed 10 trials. The trials were categorized as highly or lowly rated according to the answerer’s confidence in the answer. The task performance and behavioral and autonomic nervous activities were then compared between these categories of trials. Behavioral activity was evaluated via movements and speech activities, while for autonomic nervous activity, sympathetic nervous activity (SNA) was evaluated via skin conductance. The task performance was significantly better in the highly rated trials, whereas there were no significant differences in the behavioral activities between the highly and lowly rated trials. Moreover, in the highly rated trials, the skin conductance of the answerer was significantly high, whereas that of the supporter was significantly low. The results support the hypothesis and suggest that contrasting differences in SNA between an answerer and a supporter are indicators of the subjective ratings of answers in creative problem solving.

532 - “Mapping the Lived Experiences: The Dyad Journey of People with Agitation in Alzheimer’s and Their Care Partners”

A substantial amount of analysis has been dedicated to understanding the individual journeys of the “patient” and the “caregiver” in Alzheimer’s disease. This work has provided valuable insights, but a few priorities remain:how is the lived experience journey in Alzheimer’s shaped by the complexities of agitation and other behavioral aspects of the disease;how can insights from “social listening” analysis structure our understanding of these journeys;how can we understand the dyad journey of the person with Alzheimer’s and the care partner as well as the interactions therein, particularly through the lens of agitation.This project, “Mapping the Lived Experiences” recasts the Alzheimer’s journey to better reflect these priorities. We offer a visual interpretation of the journey with the rationale and proof points that underpin it.“Mapping the Lived Experiences” prioritizes agitation and other behavioral aspects of Alzheimer’s as pivotal, enduring challenges on the disease journey. We frame the journey into two overarching phases: “the first loss,” which accounts for the more widely recognized symptoms of the disease, such as memory loss and declining cognitive function; and “the second loss,” which is characterized by agitation and behavioral aspects of the disease. We structure the journey around “milestone moments” – moments when both the person with Alzheimer’s and the care partner recognize that the disease has taken a major progression.The “milestones” moment framework reflects an interpretive framework developed through an ongoing “social listening” research project. This social listening research allows analysis of the online conversation as it is happening in social channels and discussion boards, and it provides a poignant counterpoint to quantitative market research insofar as it illuminates the unfiltered, unmitigated experiences as articulated by the people who are learning in real-time to manage and live with Alzheimer’s. From social listening insights and analysis, our research posits that journeys – for both the person with Alzheimer’s and the care partner – are not linear, straight-line trajectories, but jagged, fragmented paths marked by “milestone moments” that shape thinking, understanding, and behavior.“Mapping the Lived Experiences” offers a dyad visualization and articulations, as it fuses the journeys of the person with lived experience and the care partner together in the same visual space. This approach reveals how, over time, these journey relate, inform, and ultimately depart from one another. This dyad offers new insights into both the lived experience and care partner journeys.

ConvLogMiner: A Real-Time Conversational Lifelog Miner

This paper presents a conversational lifelog mining system, ConvLogMiner, which detects personal life events from the human online conversation in real-time. Given a daily conversation of two speakers, ConvLogMiner identifies the new life events specific to each speaker that occur in the latest utterances. The lifelogs mined by our system are useful to provide complementary information to support lifestyle analysis and memory assistance service.

Digital conversations about depression among Hispanics and non-Hispanics in the US: A big‐data, machine learning analysis

Background Digital conversations can offer unique information into the attitudes of Hispanics with depression outside of formal clinical settings and help generate useful information for medical treatment planning. Our study aimed to explore the big data from open-source digital conversations among Hispanics with regard to depression, specifically attitudes toward depression comparing Hispanics and non-Hispanics using machine learning technology. Methods Advanced machine‐learning empowered methodology was used to mine and structure open‐source digital conversations of self‐identifying Hispanics and non-Hispanics who endorsed suffering from depression and engaged in conversation about their tone, topics, and attitude towards depression. The search was limited to 12 months originating from US internet protocol (IP) addresses. Results A total of 441, 000 unique conversations about depression, including 43,000 (9.8%) for Hispanics, were posted. Source analysis revealed that 48% of conversations originated from topical sites compared to 16% on social media. Several critical differences were noted between Hispanics and non-Hispanics. In a higher percentage of Hispanics, their conversations portray “negative tone” due to depression (66% vs 39% non-Hispanics), show a resigned/hopeless attitude (44% vs. 30%) and were about ‘living with’ depression (44% vs. 25%). There were important differences in the author's determined sentiments behind the conversations among Hispanics and non-Hispanics. Conclusion In this first of its kind big data analysis of nearly a half-million digital conversations about depression using machine learning we found that Hispanics engage in an online conversation about negative, resigned, and hopeless attitude towards depression more often than non-Hispanic.